{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from netCDF4 import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# If running locally:\n",
    "import sys\n",
    "sys.path.append('permamodel/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from permamodel.components import bmi_Ku_component"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running Ku"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example, we will run Ku for 1,000 years, using time series as inputs. This assumes a spatially-uniform distribution of input variables over the entire site, as opposed to grid inputs.\n",
    "First, make sure that the 'eightmile.cfg' file is in your inputs directory. Feel free to name it something different, or store it elsewhere, but modify the following code cell accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tell Ku where to find the input file\n",
    "cfg_file = os.path.join('./inputs/', 'eightmile.cfg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To modify the input file, open it in a text editor (JupyterHub has one built-in). Make sure that the permafrost directory, input directory, and output directory are recorded correctly. If you have renamed any input files, change those names in the configuration file as well. And, make sure to enable model output!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set up a new instance of Ku\n",
    "ku = bmi_Ku_component.BmiKuMethod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "Ku model component: Initializing...\n"
     ]
    }
   ],
   "source": [
    "# Initialize the model using the configuration file\n",
    "ku.initialize(cfg_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decide on the number of time steps\n",
    "# NOTE: If using time series inputs, nt / dt should equal the number of values in each time series\n",
    "nt = ku.get_end_time() - ku.get_start_time()\n",
    "dt = ku.get_time_step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***\n",
      "Writing output finished!\n",
      "Please look at./outputs/eightmile-ALT.nc and ./outputs/eightmile-TPS.nc\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "permamodel/permamodel/components/Ku_method.py:1266: UserWarning: WARNING: missing_value cannot be safely cast to variable dtype\n",
      "  temp.missing_value = -999.99\n"
     ]
    }
   ],
   "source": [
    "# Run the model! To interact with Ku between updates, modify this time loop\n",
    "for i in range(int(nt)):\n",
    "    ku.update()\n",
    "\n",
    "# And then finalize the model run\n",
    "ku.finalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common error here looks like: \n",
    "\n",
    "`PermissionError: [Errno 13] Permission denied: b'./outputs/eightmile-ALT.nc'`\n",
    "\n",
    "If you get this, delete the files in your outputs/ directory, then re-run the code cell above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/tljh/user/lib/python3.6/site-packages/ipykernel_launcher.py:3: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/opt/tljh/user/lib/python3.6/site-packages/ipykernel_launcher.py:6: UserWarning: WARNING: missing_value not used since it\n",
      "cannot be safely cast to variable data type\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Load the output files into arrays\n",
    "alt_nc = Dataset('./outputs/eightmile-ALT.nc')\n",
    "alt = alt_nc['data'][:,0,0]\n",
    "\n",
    "tps_nc = Dataset('./outputs/eightmile-TPS.nc')\n",
    "tps = tps_nc['data'][:,0,0]\n",
    "\n",
    "# And, let's set non-physical values to NaN\n",
    "alt[np.where(alt < 0)] = np.nan\n",
    "tps[np.where(tps < -999)] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take a look at the outputs!\n",
    "plt.plot(alt)\n",
    "plt.xlabel('Simulation Year')\n",
    "plt.ylabel('Active layer thickness in m')\n",
    "plt.title('Ku model output: Active layer thickness')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take a look at the outputs!\n",
    "plt.plot(tps)\n",
    "plt.xlabel('Simulation Year')\n",
    "plt.ylabel('Soil temperature ($^\\circ$C)')\n",
    "plt.title('Ku model output: Soil temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
